An Evolutionary Algorithm to Optimize Log/Restore Operations within Optimistic Simulation Platforms

In this work we address state recoverability in advanced optimistic simulation systems by proposing an evolutionary algorithm to optimize at run-time the parameters associated with state log/restore activities. Optimization takes place by adaptively selecting for each simulation object both (i) the best suited log mode (incremental vs non-incremental) and (ii) the corresponding optimal value of the log interval. Our performance optimization approach allows to indirectly cope with hidden effects (e.g., locality) as well as cross-object effects due to the variation of log/restore parameters for different simulation objects (e.g., rollback thrashing). Both of them are not captured by literature solutions based on analytical models of the overhead associated with log/restore tasks. More in detail, our evolutionary algorithm dynamically adjusts the log/restore parameters of distinct simulation objects as a whole, towards a well suited configuration. In such a way, we prevent negative effects on performance due to the biasing of the optimization towards individual simulation objects, which may cause reduced gains (or even decrease) in performance just due to the aforementioned hidden and/or cross-object phenomena. We also present an application-transparent implementation of the evolutionary algorithm within the ROme OpTimistic Simulator (ROOT-Sim), namely an open source, general purpose simulation environment designed according to the optimistic synchronization paradigm

Evolutionary Psychology

Evolutionary psychology (EP) is an approach to the study of the mind that is founded on Darwin’s theory of evolution by natural selection. It assumes that our mental abilities, emotions and preferences are adapted specifically for solving problems of survival and reproduction in humanity’s ancestral environment, and derives testable predictions from this assumption. This has important implications for our understanding of the conditions for human well-being

The Link between BPR, Evolutionary Delivery and Evolutionary Development

In this paper we intend to show how the challenges of managing a Business Process Reengineering (BPR) project are consistent with the ones of a Systems Development project. As traditional management techniques were no longer appropriate in the changing business environment, companies employed BPR to achieve elevated business performance. Similarly, as traditional systems development approaches delivered disappointing results, system developers experimented with other models, including Evolutionary Delivery and Evolutionary Development, in order to enable successful technology exploitation by businesses. Both these business and systems initiatives embrace elements of cultural change, management flexibility, empowerment, organisational readiness, and technology introduction in a changing environment. We will present the similarities of the two initiatives and show how progress in one initiative could contribute in the progress of the other